Integrated Modeling Technology — Minimizing Risk and Maximizing Return on Hydro

By Didier Mallieu and Martin Fuchs

The International Energy Agency predicts hydropower capacity doubling to 2000 GW by 2050 and, whilst the opportunities are great, development of hydropower undoubtedly poses complex challenges and risks. The effects of climate change are already evident and climate models are predicting even more pronounced changes for the remainder of the 21st century. With climate change affecting all parts of the water cycle, it must be a fundamental consideration in modern water resources planning, and a vital component of hydropower design.

Unlike thermal and nuclear power plants, where the revenues are mainly determined by the cost of fuel, the revenues in hydropower very much depend upon water flow rates, which are subject to significant short- and long-term fluctuations. Many hydropower projects struggle to quantify estimates and changes in climatic conditions and, as a result, suffer from either over or under design due to wrong hydrological estimates. These can result in the difference between the ultimate success or failure of a hydropower project. What can initially seem like a sound project can turn to disaster if predicted water supply is not maintained.

Using integrated modeling technology

Consultancies today are quantifying climate change and its impact on water resources to optimize hydropower plant design, minimize ecological impact and maximize economic benefits. This work has transformed how government and financing agencies make investment decisions, manage risks and plan and design more sustainable hydropower projects.

An important aspect in climate change impact studies is the assessment of uncertainty in the projections of future climate conditions. To generate the projections a cascade of models is typically used, with a certain emission scenario defining the boundary conditions for a global climate model (GCM) in which a regional climate model (RCM) of higher spatial resolution is nested. Each of these model elements is subject to uncertainty, even if a systematic bias is corrected for.

A common approach to assess this uncertainty is to perform simulations combining a large number of different emission scenarios, GCMs and RCMs. This is commonly referred to as ensemble modeling, resulting in a wide range of regional temperature and precipitation series under future climate conditions (ensembles). Typically, when using the regional climate projections as input to hydrological models, the full ensemble is considered, i.e. a large number of model runs is performed. As a result, an ensemble of flow series is also simulated.

The flow range covered by the ensemble describes the uncertainty in the modeling results and can be evaluated statistically (e.g. median and 25%-, 75%-quantiles).

In addition, energy and revenue simulations for a certain hydropower project can be done based on the full ensemble of flow series. Together with the baseline scenario (e.g. median of the results), a pessimistic case (e.g. 25%-quantile) and an optimistic case (e.g. 75%-quantile) may also be assessed. In the context of a large hydropower investment this can be highly valuable information for financial analysis and financial risk assessment.

Case study 1: The Upper Danube

The Upper Danube in Central Europe is used extensively for hydropower generation. Pà¶yry set up a precipitation runoff model for a catchment area of 100,000 km2. Historic discharge assessments focused on the past 200 years, and future runoff conditions were estimated for 30 climate scenarios up to the year 2100. The study found that pronounced changes in runoff conditions are to be expected in the 21st century, with positive and negative effects on hydropower generation in different sub catchments of the basin.

The results are highly relevant for assessing future hydropower generation of the existing hydropower fleet in Bavaria and Austria (more than 10 GW) and also in the context of the national energy strategies 2020 and the EU energy roadmap for 2050.

The study also helped to quantify the expected climate-induced changes and clearly indicated that there will be considerable regional impacts of climate change with both upside and downside potentials. However, in the near future (2021-2050), the total hydropower production of the study area will not decrease significantly.

The results of the study have also been helpful for new hydropower developments in the area. Not only for optimisation of the design, but also for constructive dialogue with NGOs, which frequently claim that new hydropower projects are no longer economically feasible, due to the perceived impact of climate change on river runoff.

During the early phase of hydropower development, managing risk is key. Early decisions impact on longer-term profitability. By creating an integrated modeling approach that combines climate projections with hydrological and economic models, more accurate estimates of long-term energy generation and revenues are possible; which in turn helps to further optimise hydro plant design.

Climate change is not always a risk for hydroelectric power development. In many cases it can also be an opportunity. The conditions for hydropower utilisation may improve significantly in regions where an increase in precipitation is expected or changes in temperature and snow conditions will lead to a more balanced flow regime. Information on the expected changes in the flow regime can be very valuable to optimise the design, with the objective to either exploit upside potentials or mitigate possible downsides.

The most important plant parameters to be adapted are installed capacity P and, in the case of large reservoirs, also the active storage S. An adaptation of these parameters requires a case specific analysis, since climate change can have different impacts on the hydrology of a project, depending on the climatic region and the catchment characteristics. The four main types of altered flow regimes are shown in the idealised schematics shown above and on page 20.

Type 1 (Figure 1 on page 20) is characteristic for regions with a significant precipitation increase leading to a general increase in runoff. Typically this also leads to higher optima of installed capacity P (especially for run-of-river schemes) and active storage S of large reservoirs.

Type 2 (Figure 2 on page 20) can be found in regions with a significant decrease in precipitation in all seasons. As a consequence, runoff and annual generation will decrease. In such cases the optimum values of installed capacity and active storage decline. A reduction of these parameters may lead to a robust design with a benefit/cost ratio only slightly below the maximum under current climate conditions.

Type 3 (Figure 3) is characterised by a precipitation and runoff increase during the flood season but less flow during the dry period, e.g. due to increased temperature and evapotranspiration. The main parameter to be refined is the active storage S. An increase of the storage might allow to compensate for the flow reduction during the dry period.

Type 4 (Figure 4) can be found in alpine regions which are governed by snowmelt. Higher temperatures lead to less snowfall and more rainfall during the winter period increasing the low flows of that season. As there is less snow accumulated during the winter months the snowmelt runoff peak in spring and summer is reduced. In such cases annual generation will increase with the optimum installed capacity being in a similar range as for historic conditions. Due to the more balanced flow regime the optimum active storage of large reservoirs typically decreases.

Case study 2: Zambezi River Basin

For the Zambezi River Basin in South-East Africa, Pà¶yry developed an online Decision Support System (DSS) to enable the assessment of climate change scenarios and water resources development expected for the 21st century. The DSS simulations showed that climate change and proposed large-scale irrigation projects will have a significant effect on the flow regime, especially in the lower part of the basin.

Using the DSS regional water resources, planning and development can be optimised with the objective of building a climate resilient system, helping to secure water availability and hydropower generation in the forthcoming decades.

Considering the expected changes in the flow regime in the Zambezi Basin the design of new hydropower projects (installed capacity, reservoir size, etc.) can be optimised accordingly, maintaining a high benefit/cost ratio.

The DSS also has potential for use in seasonal and short-term flow forecasting in order to optimise reservoir operation in a seasonal, weekly or daily planning mode. This will allow for avoidance of spillway losses and will increase the actual production of some schemes. A pilot system is currently being tested.

Currently the focus is on longer-term (>20 year) hydrological and climate projections. However, seasonal (1-12 month) forecasts are increasing and help better serve the hydropower industry in its decision making, and help companies maximise their return on investment. These forecasts – which incorporate an integrated modeling approach – have a range of applications including optimisation of seasonal reservoir operation and reduced risks in energy trading.

Industry players are also running models for short- and medium-range (1-10 day) forecasting of inflow, generation and revenues. Such forecasting tools are not only essential for daily and weekly planning and optimisation in hydropower operations, but also for flood warning systems.

Case study 3: Mekong River Basin

In the Mekong River Basin in South East Asia, Pà¶yry is developing a forecasting model which shall be used to provide early flood warnings for some major hydropower sites that are currently under construction. The model will improve flood preparedness during the construction phase and can later be integrated in the systems used for plant operation.

Especially for the reservoir schemes located on the Mekong tributaries, such a system will allow for optimisation of reservoir operation and hydropower production at the seasonal and also at the weekly and daily scale. In a liberalised electricity market, an inflow forecast and adequate planning of operation will enable selling the production with higher revenues.


This overview has put forward the case for balanced sustainability — pushing the envelope to propose solutions that offer the best possible path forward, improve resource efficiency and improve return on investment. Design is clearly a critical part, but this can only be informed by the availability of effective estimates for a project. Climate conditions have introduced greater uncertainty into this task and have necessitated it further.

Hydropower has unique characteristics among power generation assets: the lifetime of hydropower plants (generally over 100 years) exceeds by far the financial payback period of roughly 10 to 30 years. This means that such an investment will create significant wealth for a company, a country and its inhabitants for decades. That’s the essence of sustainability: providing future generations with assets instead of liabilities. That long-term benefit is only possible if correct inflow estimates make investment possible and profitable.  

Didier Mallieu is vice president of hydropower & renewable Energy, Pà¶yry and Martin Fuchs is the business manager of hydropower and renewable energy, Pà¶yry.

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